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Backward-Consistent Diffusion Sampling for Sparsely Observed PDE Inverse Problems

arXiv:2610.04624v1 Announce Type: new Abstract: Recovering Partial Differential Equation (PDE) coefficient fields from extremely sparse observations is a severely ill-posed inverse problem for which g

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Tutorials

Bayesian Entropy-based Reordering for Calibrated Diffusion Language Models

DGX agent

arXiv:2610.05125v1 Announce Type: new Abstract: Masked Diffusion Language Models (MDLMs) generate sequences by iteratively replacing masked tokens with model predictions. At each denoising step, the d

tutorialsarxiv-cs-lg

6 Oct 2026

Broaden Your Views for Self-Supervised Video Learning

arXiv:2103.16559v3 Announce Type: cross Abstract: Most successful self-supervised learning methods are trained to align the representations of two independent views from the data. State-of-the-art met

Cross-Modal Solar Image Synthesis: Adapting the Surya Foundation Model from He I 10830 {AA} to EUV Translation and Coronal Hole Segmentation

arXiv:2610.04553v1 Announce Type: cross Abstract: The long observational record of He I 10830 {AA} offers a means to investigate solar morphology before modern extreme-ultraviolet (EUV) imaging. We ad

CurveCodec 2: Skeleton-agnostic animation compression with a learned entropy model

arXiv:2610.04211v1 Announce Type: cross Abstract: Skeletal motion is stored as every joint's transform at every frame, yet most of it is implied by the body rather than by what the motion is about. Co

tutorialsarxiv-cs-ro

DexForge: High-Fidelity Physics-Informed Dexterous Retargeting

arXiv:2610.06331v1 Announce Type: new Abstract: Human demonstrations offer rich examples of precise dexterous manipulation and a promising source of robot training data. However, high-fidelity reprodu

tutorialsarxiv-cs-ro

Diffusion Transformers are Provably Optimal In-context Generators

arXiv:2610.05333v1 Announce Type: new Abstract: Generative foundation models are attracting interest for their ability to produce desired outputs from demonstrations given at inference time, without u

DiVeR: Decision-Critical Verifier Learning for VLA Test-Time Scaling

arXiv:2610.04933v1 Announce Type: cross Abstract: Scaling robot data and model capacity has improved Vision-Language-Action (VLA) policies, but further progress is constrained by the high cost of robo

Do Motion Tokenizers for Co-Speech Gesture Generation Encode Gesture Semantics?

arXiv:2610.03765v1 Announce Type: new Abstract: Discrete motion tokenizers encode motion as atomic units and are widely used for co-speech gesture generation. It remains unclear which motion propertie

Do Neural Networks Learn Structure-Preserving Maps? A Case Study in Latent-to-Hilbert Embeddings

arXiv:2610.05297v1 Announce Type: new Abstract: We ask whether a neural network can learn a structure-preserving map from a compressed latent space to a Hilbert-space representation. Using an 8-dimens

Dynamic Quadtree Tokenization and Transformer for Adaptive Mesh PDE Forecasting

arXiv:2610.04044v1 Announce Type: new Abstract: The quadratic attention cost of Vision Transformers (ViTs) forces a trade-off between spatial resolution and rollout horizon, particularly for fine-scal

Efficient Graph Generation via Direct Prediction and Flow Matching

arXiv:2610.05397v1 Announce Type: new Abstract: Generative modeling of graph-structured data is crucial for tasks ranging from drug discovery to social network simulation. Among these models, denoisin

Frequency-Decoupled Diffusion Guidance for Non-Blind Image Deblurring

arXiv:2610.06221v1 Announce Type: new Abstract: Pretrained diffusion models provide powerful image priors for training-free posterior sampling in image restoration. To guide this sampling process, fre

From Pixels, Without Pre-training: Joint Generative and Self-Supervised Representation Learning in One Model

arXiv:2610.05711v1 Announce Type: cross Abstract: Strong image generation models are conditioned on class labels, aligned to frozen pretrained encoders, or built on separately trained autoencoders. Wh

GeoFunFlow: Geometric function flow matching for joint probabilistic inference of physical fields and complex geometries

arXiv:2509.24117v2 Announce Type: replace Abstract: Inverse problems governed by partial differential equations (PDEs) arise widely in science and engineering, but are often ill-posed and limited by s

HiER-BLS: A Hierarchy-Guided and Error-Correcting Robust Incremental Broad Learning System

arXiv:2610.05834v1 Announce Type: new Abstract: Broad Learning System (BLS) supports analytical training and incremental expansion, but its growth needs guidance on which inputs new blocks should lear

How Sparse Probability Maps Shape Mixture-of-Experts Routing

arXiv:2610.06677v1 Announce Type: new Abstract: Mixture-of-experts (MoE) routers typically apply softmax to the router scores and keep the top-K experts, making every token use exactly K experts. Spar

How to scale your HEP ML models: A recipe for robust architecture comparisons at scale

arXiv:2610.06784v1 Announce Type: cross Abstract: Much of the recent progress in machine learning domains such as language models has come from scaling laws that predict performance as a function of t

Inverse Mixed-Integer Programming: Learning Constraints then Objective Functions

arXiv:2510.04455v3 Announce Type: replace-cross Abstract: Data-driven inverse optimization for mixed-integer linear programs (MILPs), which seeks to learn an objective function and constraints consist

Langevin Flow Maps: Efficient Molecular Dynamics and Transition Path Sampling

arXiv:2610.05998v1 Announce Type: new Abstract: Molecular dynamics simulations proceed by integrating the Langevin equations over many small femtosecond timesteps. This poses a challenge for estimatin

Learning Subject-Specific Anatomical Representations via Manifold Expansion: Application to Accelerated Multi-Contrast MRI

arXiv:2610.04028v1 Announce Type: new Abstract: Clinical MRI routinely acquires multiple contrast-weighted images of the same anatomy for complementary tissue characterization. However, current accele

Learning Task and Motion Plans from Real Demonstrations with Hybrid Flow Matching

arXiv:2610.04771v1 Announce Type: cross Abstract: Long-horizon mobile manipulation requires a task plan and the motion that executes it. Generative planners trained on demonstration produce both in on

Learning Topological Representations of Protein Structure and Dynamics

arXiv:2606.14737v2 Announce Type: replace-cross Abstract: Modern protein representation models support tasks such as enzyme design and drug discovery, but their reliance on static data such as sequenc

LEON: Location Embeddings from OSM Neighborhoods via Hexagonal Graph Masked Autoencoders

arXiv:2610.05497v1 Announce Type: new Abstract: Geographic information systems increasingly rely on sophisticated spatial representation learning techniques to extract meaningful patterns from complex

Less Context, Better Geometry: Masked Geometric Encoder for Robust 3D Foundation Models

arXiv:2610.06813v1 Announce Type: new Abstract: Recent progress in 3D foundation models has enabled rapid 3D reconstruction and camera calibration by leveraging learned 3D priors from vast amount of s

Lightweight Semantic EEG Foundation Model for Frozen Cross-Disorder Transfer

arXiv:2610.05503v1 Announce Type: new Abstract: Large-scale EEG foundation models have demonstrated promising transferability across neurological disorders, but often require millions of parameters an

METRO: Metric-Enhanced Token Routing Operator

arXiv:2610.05100v1 Announce Type: new Abstract: State-of-the-art neural operators scale to complex meshes via slice-and-process architectures, yet many rely on linear compatibility scores for latent t

MIRT: Transformers for Truthful Generative Auctions with Whole-feed Permutation Externalities

arXiv:2610.06559v1 Announce Type: cross Abstract: Modern online platforms commonly rank ads and organic content separately before blending them into a feed displayed to the user, overlooking externali

Neural Bayesian Filtering

arXiv:2510.03614v2 Announce Type: replace Abstract: Sequential estimation under partial observability requires tracking beliefs that may be high-dimensional, multimodal, and non-Gaussian. Classical Ba

OCL-PDE: A Generative Framework for PDE Inverse Problems with Observation-Complementary Latents

arXiv:2610.06259v1 Announce Type: new Abstract: Partial differential equation (PDE) inverse problems are often ill-posed, making fine-scale details difficult to recover. We address this problem by int

OctMesh: A Unified Octree-Hierarchical Framework for Lossless Triangle Mesh Compression

arXiv:2610.04281v1 Announce Type: new Abstract: Lossless triangle mesh compression must preserve both vertex positions and connectivity. Octrees support learned point cloud geometry coding and progres

One Token Can Be Enough: Bridging Prompting and Activation Steering with Prefix Steering

arXiv:2610.04967v1 Announce Type: new Abstract: Prompting guides language model behavior through the initial context, whereas activation steering often intervenes throughout generation. A natural ques

Optimizing Breast Cancer Detection in Mammograms: A Comprehensive Study of Transfer Learning, Resolution Reduction, and Multi-View Classification

arXiv:2503.19945v4 Announce Type: replace-cross Abstract: Mammography, an X-ray-based imaging technique, remains central to the early detection of breast cancer. Recent advances in artificial intellig

Out-of-control Hamiltonian Learning

arXiv:2610.06709v1 Announce Type: cross Abstract: Learning the Hamiltonian of a many-body system from its dynamics is a central task in quantum science, yet the algorithms with the strongest provable

Pay to Learn, Share to Earn: Incentivized Federated Multi-Player Bandits

arXiv:2610.06062v1 Announce Type: new Abstract: Federated multi-player multi-armed bandit problems model collaborative sequential decision-making where multiple players interact with a common bandit e

Prism: Dynamic Sparse Attention for Native 2K Joint Video-Audio Generation Model Training

arXiv:2610.05416v1 Announce Type: new Abstract: Natively training joint video-audio generation models at higher resolutions empowers them to learn richer visual details and sharper motion dynamics. Ho

Progressive Multi-Ancestor Bit-Depth Distillation

arXiv:2610.04100v1 Announce Type: new Abstract: Model compression strategies are widely employed to reduce memory footprint and network complexity, particularly for devices with constrained computatio

Protecting Sensitive Data in Image Synthesis via PAC-Private Adaptation for Diffusion Models

arXiv:2610.04038v1 Announce Type: new Abstract: Synthetic data are increasingly used as an alternative to sharing sensitive records. However, synthetic data generation does not guarantee privacy, as d

Pythia: Toward Foundation World Models for Multimodal Time Series

arXiv:2610.05240v1 Announce Type: new Abstract: Time-series foundation models offer a unified approach to forecasting across heterogeneous domains. Textual context and auxiliary observations provide c

Questioning the Questions: Sustaining Self-Evolution in Reasoning Models

arXiv:2610.04299v1 Announce Type: new Abstract: Self-evolving reasoning models learn from their own generated questions, yet repeated self-training can lead to performance collapse. In this paper, we

Robotic Long-Horizon Manipulation with Progressive In-Context Code Generation and Episodic Feedback

arXiv:2503.21969v4 Announce Type: replace Abstract: Robotic long-horizon manipulation requires robots to compose perception, reasoning, and action over extended task sequences, yet existing language-c

tutorialsarxiv-cs-ro

S^3N: A Spherical Spiral Scanning Network for Weather Forecasting

arXiv:2610.04338v1 Announce Type: new Abstract: Machine learning-based weather prediction (MLWP) has achieved strong performance in global weather forecasting. Recent Hierarchical Equal Area isoLatitu

SimAuthor: Harnessing Foundation Models for Persistent Scientific Simulator Authoring

arXiv:2610.06257v1 Announce Type: new Abstract: Foundation models can generate scientific code, but authoring a scientific simulator (an executable program encoding hypotheses about how mechanisms gen

TMAML: Temporal Model-Agnostic Meta-Learning for Cold-Start Time Series Forecasting

arXiv:2610.04547v1 Announce Type: new Abstract: Cold-start forecasting, the task of forecasting a time series with little to no historical data, is a common challenge. Addressing it requires approache

To Learn is to Wander: Learning Across Graphs and Tasks with Random Walks

arXiv:2610.06694v1 Announce Type: new Abstract: Graph foundation models aim to transfer across graphs, feature spaces, relational schemas, and prediction tasks, yet existing approaches typically gener

Towards Looped Models Done Right, Part II: Rethinking at Fixed Points

arXiv:2610.06833v1 Announce Type: new Abstract: Every recurrence of a looped language model adds cost in training, decoding, prefill, and reinforcement learning (RL). The closer recurrent states get t

Training Numerical Intelligence via Auto-Diagnosis and Skill Discovery

arXiv:2610.03872v1 Announce Type: cross Abstract: AI agents are becoming increasingly capable of generating scientific code, but generating code is not the same as improving the algorithms behind it.

Ultrasound Operator Guidance Using World Modeling and Retrieval Based Action Planning

arXiv:2610.06008v1 Announce Type: cross Abstract: Ultrasound is widely used, but acquisition quality is heavily dependent on the operator's knowledge and expertise. With demand for examinations outpac